A computationally efficient surrogate model based robust optimization for permanent magnet synchronous machines
Sprache des Titels:
Englisch
Original Kurzfassung:
One of the main obstacles to the robust optimization
for permanent magnet machines is the high computational
burden, which is mainly caused by the robustness evaluation
process considering the manufacturing uncertainties. In this
paper, a sequential sampling Kriging model is adopted to estimate
the design robustness, and the problem of high-dimensional
variables for the surrogate model due to uncertainties is avoided
through adopting the worst-case based approach. To further
reduce the number of finite element analyses (FEA) required
for constructing a metamodel, a multi-points sequential sampling
process with a two-step optimization oriented surrogate model
updating algorithm is proposed. Compared with the FEA directly
based robust optimization in previous work, similar Pareto Fronts
are achieved by adopting the meta-model proposed algorithm but
the overall run time reduced by 75%. In the end, an optimization
problem for a 1.2 kW motor is considered by applying the pro
posed algorithm, and two prototypes with deliberately designed
tolerances, imitating the worst-case scenarios in the real world,
are manufactured. The worst-case torque ripple is reduced from
76% to 48% after optimizing, and this verifies the efficacy of
the proposed algorithm.
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